{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "view-in-github"
   },
   "source": [
    "<a href=\"https://colab.research.google.com/github/jermwatt/machine_learning_refined/blob/main/notes/6_Linear_twoclass_classification/6_8_Metrics.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "BJdYFdiopH8O"
   },
   "source": [
    "## Chapter 6: Linear two-class classification"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook contains interactive content from an early draft of the university textbook <a href=\"https://github.com/neonwatty/machine-learning-refined/tree/main\">\n",
    "Machine Learning Refined (2nd edition) </a>.\n",
    "\n",
    "The final draft significantly expands on this content and is available for <a href=\"https://github.com/neonwatty/machine-learning-refined/tree/main/chapter_pdfs\"> download as a PDF here</a>."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "IlpqvTMEpH8P"
   },
   "source": [
    "# 6.8 Classification Quality Metrics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "id": "fArvjnFWpH8Q"
   },
   "source": [
    "In this Section we describe simple metrics for judging the quality of a trained two-class classification model, as well as how to make predictions using one."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "_7Y0TrrRpH8Q",
    "outputId": "07309738-3546-4d1c-c3a8-38f0cf07d170"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: github-clone in /Users/jeremywatt/Desktop/machine-learning-refined/venv/lib/python3.10/site-packages (1.2.0)\n",
      "Requirement already satisfied: requests>=2.20.0 in /Users/jeremywatt/Desktop/machine-learning-refined/venv/lib/python3.10/site-packages (from github-clone) (2.32.3)\n",
      "Requirement already satisfied: docopt>=0.6.2 in /Users/jeremywatt/Desktop/machine-learning-refined/venv/lib/python3.10/site-packages (from github-clone) (0.6.2)\n",
      "Requirement already satisfied: charset-normalizer<4,>=2 in /Users/jeremywatt/Desktop/machine-learning-refined/venv/lib/python3.10/site-packages (from requests>=2.20.0->github-clone) (3.4.0)\n",
      "Requirement already satisfied: idna<4,>=2.5 in /Users/jeremywatt/Desktop/machine-learning-refined/venv/lib/python3.10/site-packages (from requests>=2.20.0->github-clone) (3.10)\n",
      "Requirement already satisfied: urllib3<3,>=1.21.1 in /Users/jeremywatt/Desktop/machine-learning-refined/venv/lib/python3.10/site-packages (from requests>=2.20.0->github-clone) (2.2.3)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in /Users/jeremywatt/Desktop/machine-learning-refined/venv/lib/python3.10/site-packages (from requests>=2.20.0->github-clone) (2024.12.14)\n",
      "\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m24.2\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n",
      "chapter_6_datasets already cloned!\n",
      "chapter_6_library already cloned!\n",
      "chapter_6_images already cloned!\n"
     ]
    }
   ],
   "source": [
    "# install github clone - allows for easy cloning of subdirectories\n",
    "!pip install github-clone\n",
    "from pathlib import Path \n",
    "\n",
    "# clone datasets\n",
    "if not Path('chapter_6_datasets').is_dir():\n",
    "    !ghclone https://github.com/neonwatty/machine-learning-refined-notes-assets/tree/main/notes/6_Linear_twoclass_classification/chapter_6_datasets\n",
    "else:\n",
    "    print('chapter_6_datasets already cloned!')\n",
    "\n",
    "# clone library subdirectory\n",
    "if not Path('chapter_6_library').is_dir():\n",
    "    !ghclone https://github.com/neonwatty/machine-learning-refined-notes-assets/tree/main/notes/6_Linear_twoclass_classification/chapter_6_library\n",
    "else:\n",
    "    print('chapter_6_library already cloned!')\n",
    "\n",
    "# clone images\n",
    "if not Path('chapter_6_images').is_dir():\n",
    "    !ghclone https://github.com/neonwatty/machine-learning-refined-notes-assets/tree/main/notes/6_Linear_twoclass_classification/chapter_6_images\n",
    "else:\n",
    "    print('chapter_6_images already cloned!')\n",
    "\n",
    "# append path for local library, data, and image import\n",
    "import sys\n",
    "sys.path.append('./chapter_6_library')\n",
    "sys.path.append('./chapter_6_datasets') \n",
    "sys.path.append('./chapter_6_images') \n",
    "\n",
    "# import section helper\n",
    "import section_6_8_helpers\n",
    "\n",
    "# dataset paths\n",
    "data_path_1 = 'chapter_6_datasets/3d_classification_data_v0.csv'\n",
    "\n",
    "# image paths\n",
    "image_path_1 = 'chapter_6_images/Fig_4_6_new.png'\n",
    "image_path_2 = 'chapter_6_images/confusion_2class.png'\n",
    "\n",
    "# standard imports\n",
    "import matplotlib.pyplot as plt\n",
    "from IPython.display import Image, HTML\n",
    "\n",
    "# import autograd-wrapped numpy\n",
    "import autograd.numpy as np\n",
    "\n",
    "# this is needed to compensate for matplotlib notebook's tendancy to blow up images when plotted inline\n",
    "%matplotlib inline\n",
    "from matplotlib import rcParams\n",
    "rcParams['figure.autolayout'] = True\n",
    "\n",
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "rNtk5ITbpH8R"
   },
   "source": [
    "## Making predictions using a trained model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "zMI6XxgnpH8S"
   },
   "source": [
    "If we denote the optimal set of weights found by minimizing a classification cost function, employing by default label values $y_p \\in \\left\\{-1,+1\\right\\}$, by $\\mathbf{w}^{\\star}$ then note we can write our fully tuned linear model as \n",
    "\n",
    "\\begin{equation}\n",
    "\\text{model}\\left(\\mathbf{x},\\mathbf{w}^{\\star}\\right) =  \\mathring{\\mathbf{x}}_{\\,}^T\\mathbf{w}^{\\star}   = w_0^{\\star} + x_{1}^{\\,}w_1^{\\star} + x_{2}^{\\,}w_2^{\\star} + \\cdots + x_{N}^{\\,}w_N^{\\star}.\n",
    "\\end{equation}\n",
    "\n",
    "This fully trained model defines an optimal decision boundary for the training dataseet which takes the form\n",
    "\n",
    "\\begin{equation}\n",
    "\\text{model}\\left(\\mathbf{x},\\mathbf{w}^{\\star}\\right) = 0.\n",
    "\\end{equation}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "SiJsyla7pH8S"
   },
   "source": [
    "To predict the label $y$ of an input $\\mathbf{x}$ we then process this model through an appropriate step function. Since we by default use label values $y_p \\in \\left\\{-1,+1\\right\\}$ this step function is conveniently defined by the $\\text{sign}\\left(\\cdot\\right)$ function (as detailed in [Section 6.3](https://jermwatt.github.io/machine_learning_refined/notes/6_Linear_twoclass_classification/6_3_Softmax.html)), and the predicted label for $\\mathbf{x}$ is given as\n",
    "\n",
    "\\begin{equation}\n",
    "\\text{sign}\\,\\left(\\text{model}\\left(\\mathbf{x},\\mathbf{w}^{\\star}\\right)\\right) = y_{\\,}.\n",
    "\\end{equation}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "HTX--SBppH8S"
   },
   "source": [
    "This evaluation - which will always take on values $\\pm 1$ if $\\mathbf{x}$ does not lie *exactly* on the decision boundary (in which case we assign a random value from $\\pm 1$) - simply computes which side of the decision boundary the input $\\mathbf{x}$ lies on.  If it lies 'above' the decision boundary then $y = +1$, and if 'below' then $y = -1$.  This is illustrated in the figure below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "DOcJXYz9pH8T",
    "outputId": "a8966aab-6d52-46c1-dbd5-a69e130ce1a1"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<img src=\"chapter_6_images/Fig_4_6_new.png\" width=\"90%\" height=\"auto\" alt=\"\"/>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import HTML\n",
    "HTML('''<img src=\"''' + image_path_1 + '''\" width=\"90%\" height=\"auto\" alt=\"\"/>''')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 290
    },
    "id": "9YuV6eOSpedA",
    "outputId": "cf8b6a19-eb47-4d6b-ad0b-98da2b8a5d78"
   },
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 3,
     "metadata": {
      "image/png": {
       "width": 900
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(image_path_1, width=900)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ppQniR1XpH8U"
   },
   "source": [
    "<figure>\n",
    "  <figcaption>   \n",
    "<strong>Figure 3:</strong> <em> Once a decision boundary has been learned for the training dataset with optimal parameters $w_0^{\\star}$ and $\\mathbf{w}^{\\star}$, the label $y_{\\,}$ of a new point $\\mathbf{x}_{\\,}$ can be predicted by simply checking which side of the boundary it lies on. In the illustration shown here $\\mathbf{x}_{\\,}$ lies below the learned hyperplane, and as a result is given the label $\\textrm{sign}\\left(\\mathring{\\mathbf{x}}_{\\,}^{T}\\mathbf{w}^{\\star}\\right)=-1$. </em>  </figcaption> \n",
    "</figure>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "JcUTcKccpH8U"
   },
   "source": [
    "## Confidence scoring"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "pfOLp9vepH8U"
   },
   "source": [
    "Once a proper decision boundary is learned, we can judge its *confidence* in any point based on *the point's distance to the decision boundary*.  We say that our classifier has *zero* confidence in points lying along the decision boundary itself, because the boundary cannot tell us accurately which class such points belong too (which is why they are randomly assigned a label value if we ever need to make a prediction there).  Likewise we say that *near* the decision boundary we have *little confidence* in the classifier's predictions.  Why is this?  Imagine we apply a small pertibation to the decision boundary, changing its location ever so slightly.  Some points *very close to the original boundary* will end up on the *opposite* side of the new boundary, and will consequently have *different predicted labels*.  Conversely, this is why we have *high confidence* in the predicted labels of points *far* from the decision boundary.  These predicted labels will not change if we make a small change to the location of the decision boundary."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "pvtlchBApH8V"
   },
   "source": [
    "The notion of 'confidence' can be made precise and normalized to be universally applicable by running the point's distance to the boundary through the *sigmoid function* (see  [Section 6.2](https://jermwatt.github.io/machine_learning_refined/notes/6_Linear_twoclass_classification/6_2_Cross_entropy.html)).  This gives the confidence that a point belongs to class $+1$.\n",
    "\n",
    "The signed distance $d$ from a point to the decision boundary provided by our trained model can be computed (see [Section 6.4](https://jermwatt.github.io/machine_learning_refined/notes/6_Linear_twoclass_classification/6_4_Perceptron.html)) as\n",
    "\n",
    "\\begin{equation}\n",
    "d =  \\frac{b^{\\star} + \\overset{\\,}{\\mathbf{x}}_{p}^T\\boldsymbol{\\omega^{\\star}} }{\\left\\Vert \\overset{\\,}{\\boldsymbol{\\omega}^{\\star}} \\right\\Vert_2 }.\n",
    "\\end{equation}\n",
    "\n",
    "where we denote\n",
    "\n",
    "\\begin{equation}\n",
    "\\text{(bias):}\\,\\, b^{\\star} = w_0^{\\star} \\,\\,\\,\\,\\,\\,\\,\\, \\text{(feature-touching weights):} \\,\\,\\,\\,\\,\\, \\boldsymbol{\\omega^{\\star}} = \n",
    "\\begin{bmatrix}\n",
    "w_1^{\\star} \\\\\n",
    "w_2^{\\star} \\\\ \n",
    "\\vdots \\\\\n",
    "w_N^{\\star}\n",
    "\\end{bmatrix}.\n",
    "\\end{equation}\n",
    "\n",
    "By evaluating $d$ using the *sigmoid function* \n",
    "\n",
    "\\begin{equation}\n",
    "\\text{confidence in the predicted label of a point $\\mathbf{x}$} \\,\\,\\, =  \\sigma\\left(d\\right)\n",
    "\\end{equation}\n",
    "\n",
    "we squash it smoothly onto the interval $\\left[0,1\\right]$.  \n",
    "\n",
    "When this value equals $0.5$ the point lies on the boundary itself.  If the value is greater than $0.5$ the point lies on the positive side of the decision boundary and so we have larger confidence in its predicted label being $+1$.  When the value is less than $0.5$ the point lies on the *negative* side of the classifier, and so we have less confidence that it truly has label value $+1$.  Because normalization employing the sigmoid squashes $\\left(-\\infty,+\\infty \\right)$ down to the interval $\\left[0,1 \\right]$ this confidence value is often interpreted as a *probability*. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "QgLk9-HfpH8V"
   },
   "source": [
    "## Judging the quality of a trained model using *accuracy*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Uk1iLk9gpH8V"
   },
   "source": [
    "Once we have successfully minimized the a cost function for linear two-class classification it can be a delicate matter to determine our trained model's quality.  The simplest metric for judging the quality of a fully trained model is to simply *count the number of misclassifications* it forms over our training dataset.  This is a raw count of the number of training datapoints $\\mathbf{x}_p$ whose true label $y_p$ is predicted *incorrectly* by our trained model.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "JcJbWbANpH8V"
   },
   "source": [
    "To compare the point $\\mathbf{x}_p$'s predicted label $\\hat{y}_p = \\text{sign}\\,\\left(\\text{model}\\left(\\mathbf{x}_p,\\mathbf{w}^{\\star}\\right)\\right)$ and true true label $y_p$ we can use an identity function $\\mathcal{I}\\left(\\cdot\\right)$ and compute\n",
    "\n",
    "\\begin{equation}\n",
    "\\mathcal{I}\\left(\\hat{y}_p,\\,\\overset{\\,}{y}_p\\right) = \n",
    "\\begin{cases}\n",
    "0 \\,\\,\\,\\,\\,\\text{if} \\,\\, \\hat{y}_p,= \\overset{\\,}{y}_p \\\\\n",
    "1 \\,\\,\\,\\,\\,\\text{if} \\,\\,  \\hat{y}_p,\\neq \\overset{\\,}{y}_p. \\\\\n",
    "\\end{cases}\n",
    "\\end{equation}\n",
    "\n",
    "Summing all $P$ points gives the total number of misclassifications of our trained model\n",
    "\n",
    "\n",
    "\\begin{equation}\n",
    "\\text{number of misclassifications} = \\sum_{p=1}^P \\mathcal{I}\\left(\\hat{y}_p,\\,\\overset{\\,}{y}_p\\right).\n",
    "\\end{equation}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "gYQHCovtpH8V"
   },
   "source": [
    "Using this we can also compute the *accuracy*  - denoted $\\mathcal{A}$ - of a trained model.  This is simply the percentage of training dataset whose labels are correctly predicted by the model.\n",
    "\n",
    "\\begin{equation}\n",
    "\\mathcal{A}= 1 - \\frac{1}{P}\\sum_{p=1}^P \\mathcal{I}\\left(\\hat{y}_p,\\,\\overset{\\,}{y}_p\\right).\n",
    "\\end{equation}\n",
    "\n",
    "The accuracy ranges from 0 (no points are classified correctly) to 1 (all points are classified correctly).  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "82PjxCljpH8W"
   },
   "source": [
    "#### <span style=\"color:#a50e3e;\">Example 1: </span> Comparing the Softmax to the number of misclassifications during runs of gradient descent"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "URxQ5P0CpH8W"
   },
   "source": [
    "Our classification cost functions are - in the end - based on smooth approximations to a discrete step function.  This is the function we trully wish to use, i.e., the function through which we truly want to tune the parameters of our model.  However since we cannot easily optimize this step function - as discussed in Sections [6.2](https://jermwatt.github.io/machine_learning_refined/notes/6_Linear_twoclass_classification/6_2_Cross_entropy.html) and [6.3](https://jermwatt.github.io/machine_learning_refined/notes/6_Linear_twoclass_classification/6_3_Softmax.html) - we settle for a smooth approximation.  The consequences of this practical choice are seen when we compare the cost function history from a run of gradient descent to the corresponding misclassification count measured at each step of the run.  We show such a comparison using  the dataset from Example 1 of [Section 6.4](https://jermwatt.github.io/machine_learning_refined/notes/6_Linear_twoclass_classification/6_4_Perceptron.html).  In fact we show such results of three independent runs of gradient descent, with the history of misclassifications shown in the left panel and corresponding Softmax cost histories show in the right panel.  Each run is color-coded to distinguish it from the other runs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 297
    },
    "id": "b61HfZeXpH8W",
    "outputId": "c3d367e3-2562-423a-f8e4-f7ce0483affc"
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 800x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# load in dataset\n",
    "data = np.loadtxt(data_path_1, delimiter = ',')\n",
    "\n",
    "# create instance of cost comparison demo\n",
    "demo = section_6_8_helpers.visualizer(data)\n",
    "\n",
    "# run \n",
    "demo.compare_to_counting(cost = 'softmax',max_its = 500,num_runs = 3,alpha = 10**(-1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "pyq7O4wXpH8X"
   },
   "source": [
    "Comparing the left and right panels we can see that the number of misclassifications and Softmax evaluations at each step of a gradient descent run do not perfectly track one another.  That is, it is not the case that just because the cost function value is decreasing that so too is the number of misclassifications. Again, this occurs because our Softmax cost is only an approximation of the true quantity we would like to minimize.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "vQqGH4hhpH8X"
   },
   "source": [
    "This simple example has an extremely practical implication: after a running a local optimization to minimize a two-class classification cost function the best step, and corresponding weights, are associated with the lowest *number of misclassifications* (or likewise the *highest accuracy*) **not** the lowest cost function value."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "id": "zy4Cnw4epH8X"
   },
   "source": [
    "## Judging the quality of a trained model using *balanced accuracy*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "aznrxQqppH8X"
   },
   "source": [
    "Classification accuracy is an excellent basic measurement of a trained classifier's performance.  However in certain scenarios using the accuracy metric can paint an incomplete picture of how well we have really solved a classification problem.  For example when a dataset consists of *highly imbalanced classes*  - that is when a dataset has far more examples of one class than the other - the 'accuracy' of a trained model loses its value as a metric.  This is because when one class greatly outnumbers the other in a dataset an accuracy value close to 1 can be misleading: if one class makes up $95\\%$ percent of all data points, a naive classifier that blindly assigns the label of the majority class to *every training point* achieves an accuracy of $95\\%$.  But here misclassifying $5\\%$ amounts to *completely misclassifying an entire class of data*. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "SnNYxsCtpH8Y"
   },
   "source": [
    "This imaginary scenario points to the main problem with the use of *accuracy* as a proper metric for diagnosing classifier performance on datasets with highly imbalanced classes: because it weights *misclassifications from both classes equally* it fails to convey how well a trained model performs on each class of the data individually.  This results in the potential for strong performance on a very large class of data masking poor performance on a very smaller one.  The simplest way to improve the accuracy metric to take this potential problem into account is, instead of computing accuracy over *both classes together*, to compute accuracy on *each class individually* and combine the resulting metrics.  \n",
    "\n",
    "If we denote the *indices* of those points with labels $y_p = +1$ and $y_p = -1$ as $\\Omega_{+1}$ and $\\Omega_{-1}$ respectively, then we can compute the number of misclassifications on each class individually (employing the notation and indicator function introduced above) as \n",
    "\n",
    "\\begin{equation}\n",
    "\\begin{array}\n",
    "\\\n",
    "\\text{number of misclassifications on $+1$ class} = \\sum_{p \\in \\Omega_{+1}} \\mathcal{I}\\left(\\hat{y}_p,\\,\\overset{\\,}{y}_p\\right) \\\\\n",
    "\\text{number of misclassifications on $-1$ class} = \\sum_{p \\in \\Omega_{-1}} \\mathcal{I}\\left(\\hat{y}_p,\\,\\overset{\\,}{y}_p\\right).\n",
    "\\end{array}\n",
    "\\end{equation}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_hVWn4CCpH8Y"
   },
   "source": [
    "The accuracy on each class individually can then be likewise computed as (denoting the accuracy on class $+1$ and $-1$ individually as $\\mathcal{A}_{+1}$ and $\\mathcal{A}_{-1}$ respectively)\n",
    "\n",
    "\\begin{equation}\n",
    "\\begin{array}\n",
    "\\\n",
    "\\mathcal{A}_{+1} = 1 - \\frac{1}{\\left\\vert \\Omega_{+1}\\right\\vert}\\sum_{p \\in \\Omega_{+1}} \\mathcal{I}\\left(\\hat{y}_p,\\,\\overset{\\,}{y}_p\\right) \\\\\n",
    "\\mathcal{A}_{-1}= 1 - \\frac{1}{\\left\\vert \\Omega_{-1}\\right\\vert}\\sum_{p \\in \\Omega_{-1}} \\mathcal{I}\\left(\\hat{y}_p,\\,\\overset{\\,}{y}_p\\right) \n",
    "\\end{array}\n",
    "\\end{equation}\n",
    "\n",
    "Note here the $\\left\\vert \\Omega_{+1}\\right\\vert$ and $\\left\\vert \\Omega_{-1}\\right\\vert$ denote the number of points belonging to the $+1$ and $-1$ class respectively.  We can then combine these two metrics into a single value by *taking their average*.  This combined metric is called *balanced accuracy* (which we denote as $\\mathcal{A}_{\\text{balanced}}$)\n",
    "\n",
    "\\begin{equation}\n",
    "\\mathcal{A}_{\\text{balanced}} = \\frac{\\mathcal{A}_{+1} + \\mathcal{A}_{-1}}{2}.\n",
    "\\end{equation}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "cK1q-eZwpH8Y"
   },
   "source": [
    "Notice that if we return to our motivating scenario above - where we imagined a highly imbalanced dataset with $95\\%$ membership in one class and $5\\%$ in the other and have simply classified the entire space as the majority class to achieve a $95\\%$ accuracy metric - we can capture the fact that one class has been completely misclassified with the individual class accuracy measurements above.  e.g., suppose that the $+1$ class is the majority then while the *overall accuracy* is $\\mathcal{A} = 95\\%$, the accuracy on each class individually is $\\mathcal{A}_{+1} = 1$ and $\\mathcal{A}_{-1} = 0$ respectively.  These metrics accurately reflect the fact that the naive classifier correctly classifies the entire $+1$ class, but incorrectly classifies the entire $-1$ class.  Their average - the balanced accuracy metric - also captures this fact since it then takes on the value $\\mathcal{A}_{\\text{balanced}} = 0.5$.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Xt-hz3k5pH8Y"
   },
   "source": [
    "The balanced accuracy metric ranges from $0$ to $1$.  When equal to $0$ no point is classified correctly, and when both classes are classified perfectly $\\mathcal{A}_{\\text{balanced}} = 1$.  Note: if both classes are classified equally well the balanced accuracy reduces to the overall accuracy value $\\mathcal{A}$.  Values of the metric in between $0$ and $1$ measure how well - on average - eaach class is classified individually: e.g., as we have seen $\\mathcal{A}_{\\text{balanced}} = 0.5$ can occur one class is completely misclassified while the other is classified perfectly.  Balanced accuracy is a simple metric for helping us understand whether our learned model has 'behaved poorly' on highly imbalanced datasets.  In order to *improve the behavior* of our learned model in such instances we have to adjust the way we perform two class classification.  One popular way of doing this - called weighted classification - is discussed in the next Section."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "oSzkb5kCpH8Y"
   },
   "source": [
    "##  The confusion matrix and additional quality metrics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "AeUh1-hipH8Y"
   },
   "source": [
    "Additional metrics for judging the quality of a trained model for two class classification can be formed using the *confusion matrix*, shown figuratively in the illustration below.  A confusion matrix is a simple lookup table where classification results are broken down by actual (across rows) and predicted (across columns) class membership. Here we denote $A$ is the number of data points whose actual label, +1, is identical to the label assigned to them by the trained classifier. The other diagonal entry $D$ is similarly defined as the number of data points whose predicted class, -1, is equal to their actual class. The off-diagonal entries denoted by $B$ and $C$ represent the two types of classification errors wherein the actual and predicted labels do not match one another.  In practice we want these two values to be as small as possible."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 145
    },
    "id": "CR8uzgzepmqC",
    "outputId": "e431b57d-7f7b-44fa-fbb3-6ba83900ab14"
   },
   "outputs": [
    {
     "data": {
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    "Image(image_path_2, width=200)"
   ]
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    "<figure>\n",
    "  <figcaption>   \n",
    "<strong>Figure 2:</strong> <em> A generic *confusion matrix* as a metric for classification quality.</em>  </figcaption> \n",
    "</figure>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "uhyZqthhpH8Z"
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   "source": [
    "Our *accuracy* metric can be expressed in terms of the confusion matrix quantities as\n",
    "\n",
    "\\begin{equation}\n",
    "\\mathcal{A}=\\frac{A+D}{A+B+C+D},\n",
    "\\end{equation}\n",
    "\n",
    "and our accuracy on each individual class likewise as \n",
    "\n",
    "\\begin{equation}\n",
    "\\begin{array}\n",
    "\\\n",
    "\\mathcal{A}_{+1} = \\frac{A}{A + C} \\\\\n",
    "\\mathcal{A}_{-1}= \\frac{D}{B + D}.\n",
    "\\end{array}\n",
    "\\end{equation}\n",
    "\n",
    "In the jargon of machinee learning these individual accuracy metrics are often called *precision* and *specificity* respectively.  The *balanced accuracy* metric can likewise be expressed as \n",
    "\n",
    "\\begin{equation}\n",
    "\\mathcal{A}_{\\text{balanced}} = \\frac{1}{2}\\frac{A}{A + C} + \\frac{1}{2}\\frac{D}{B + D}.\n",
    "\\end{equation}"
   ]
  }
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